Open Data for Global Multimodal Land Use Classification: Outcome of the 2017 IEEE GRSS Data Fusion Contest

Naoto Yokoya, Pedram Ghamisi, Junshi Xia, Sergey Sukhanov, Roel Heremans, Ivan Tankoyeu, Benjamin Bechtel, Bertrand Le Saux, Gabriele Moser, Devis Tuia*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

91 Citations (Scopus)


In this paper, we present the scientific outcomes of the 2017 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The 2017 Contest was aimed at addressing the problem of local climate zones classification based on a multitemporal and multimodal dataset, including image (Landsat 8 and Sentinel-2) and vector data (from OpenStreetMap). The competition, based on separate geographical locations for the training and testing of the proposed solution, aimed at models that were accurate (assessed by accuracy metrics on an undisclosed reference for the test cities), general (assessed by spreading the test cities across the globe), and computationally feasible (assessed by having a test phase of limited time). The techniques proposed by the participants to the Contest spanned across a rather broad range of topics, and of mixed ideas and methodologies deriving from computer vision and machine learning but also deeply rooted in the specificities of remote sensing. In particular, rigorous atmospheric correction, the use of multidate images, and the use of ensemble methods fusing results obtained from different data sources/time instants made the difference.
Original languageEnglish
Pages (from-to)1363-1377
JournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Issue number5
Publication statusPublished - 16 Apr 2018


  • Convolutional neural networks (CNNs)
  • crowdsourcing
  • deep learning (DL)
  • ensemble learning
  • image analysis and data fusion (IADF)
  • multimodal
  • multiresolution
  • multisource
  • OpenStreetMap (OSM)
  • random fields


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